Abstract
In Recommender Systems, recommendation tasks are usually implemented by preference mining. Most existing methods in context-aware recommendation focus on learning user interests from all the records to implement preference mining. However, some records contribute to recommendations, whereas some others may reduce the performances. To address these limitations, we propose a division learning strategy to divide the original records into several groups based on regression tree techniques. Then, a two-layer preference mining process is carried out to produce group and local preferences. Finally, the two preferences are integrated by the preference integration (Prin) approach to give recommendations. The experimental results demonstrated that our model outperformed other state-of-the-art methods, which illustrated the importance of targeted modeling.
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Acknowledgments
This research is supported by the National Natural Science Foundation of China with Grant No. 61272277. We acknowledge the editors and other anonymous reviewers for insightful suggestions on this work.
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Zheng, L., Zhu, F. (2017). Preference Integration in Context-Aware Recommendation. In: Candan, S., Chen, L., Pedersen, T., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10177. Springer, Cham. https://doi.org/10.1007/978-3-319-55753-3_30
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